Media providers typically provide a search function to allow users to search for desirable media assets. In order to provide more accurate search results, an interactive media guidance application may maintain a knowledge graph that includes contextual links between symbols. For example, a media guidance application may analyze historical search data and utilize a probabilistic classifier, such as a Naïve Bayes Classifier that determines the statistical probability that certain symbols relate to a certain topic. In this manner, the media guidance application may build a database that identifies related topics and symbols that may be used to return relevant search results to a user search query.
With the advent of social media, topics often explode in popularity for a relatively short period of time. Such topics are typically referred to as “trending” or “viral” topics. The accuracy of search algorithms may be improved by taking into account the “trending” status of topics and their related symbols.